Hyperspectral imaging is a spectral imaging technique frequently used in scientific and military applications. Hyperspectral imaging generates an intensity vs wavelength map of a target area that enables various analyses to be performed such as chemical detection and identification, mineral detection, color or paint signature identification, geologic feature identification, and crop health assessment. Hyperspectral imaging also has non-aerial applications such as hazardous materials detection, tissue scanning, anti-counterfeiting, waste detection, and quality control.
Hyperspectral imaging sensors may use tens to hundreds of contiguous spectral channels to resolve light with finer spectral resolution than is possible with multispectral sensors that only use a handful of discrete spectral bands. The finer spectral resolution enables hyperspectral sensors to detect and identify materials that often cannot be unambiguously identified with multispectral sensors. Hyperspectral imaging sensors, by design, have multitude of spectral bands that can produce well sampled spectral data resembling spectra acquired with laboratory spectrometers. To interpret the raw spectra data, a conversion process is typically performed to calibrate and transform the signal data to physical units of radiance, which is a measure of energy passing through the input aperture of the sensor at a given time as a function of the wavelength and sensor viewing angle. The hyperspectral imager may be thought of as a means for remotely acquiring spectral profiles of defined spatial areas on the object or field being observed.
Depending on the observed wavelengths, the radiance data can then be converted to reflectance data for reflective spectral analysis or to temperature and emissivity data for thermal spectral analysis. Each of these steps can include programmable compensation factors to account for atmospheric thermal emission, transmission, and scatter caused by various atmospheric constituents between the surveyed area and the hyperspectral imaging sensor.
For example, to obtain accurate reflectance spectra of the surveyed area from the raw spectral data, effects such as solar illumination, atmospheric scattering and absorption, etc., must be accounted for and removed from the raw reflectance spectra. Once atmospheric compensation has been performed, the generated reflective spectra can be compared with reflective spectra of known materials for identification.